Power in Numbers: How Data Shapes the Tax Policy Arena

Since the first number value systems were invented around 3400 BC, numbers have played a central role in human society. Arguably, though, they have never been more important than they are today. We fiend for data, quantities, statistics and numbers to an astounding degree. We live in an almost fetishised data-heavy society.

This applies to the tax world as well, of course. In fact, numbers and data are absolutely central to tax policy debates. We obsess over tax rates and the amount of tax lost to evasion and the percentage-growth effects of tax policy changes. We rank national tax regimes’ competitiveness and secrecy jurisdictions and tax avoiding companies. Etc. etc. etc.

Why? Because numbers allow us to quantify anything, communicate easily, analyse and conclude with simple outputs. And so, often times we absorb information and arguments construed as numbers over other types of inputs.

But numbers are not neutral. Numbers provide authority in a way that words or plain language sometimes does not. Numbers indicate “we did the math”, summoning ethos appeals that often provide instant credibility.

Thus, those able to produce, use and leverage numbers and data often hold greater sway, and they manage to be influential, impactful and effective in modern debates around tax. The point is similar to one I have made previously on technicisation of global tax policy processes and I think the two are related: This particular feature of the tax policy arena – an affinity for numbers – provides an environment where certain actors and arguments are often favoured.

How numbers rule

A recent paper by Hans Krause Hansen and Tony Porter in the journal International Political Sociology discusses the distinct features of numbers and how they affect transnational governance:

Stability: The meaning of numbers is less complex than words. 83 is less frequently interpreted in different ways than ‘democracy’ or even ‘house’.

Combinability: Numbers are easier to combine, also in different ways. 2+2 is always 4; five oranges and two bananas can be 7 or 5+2.

Order: Quantitative ranking is intuitive and comprehensible. 1 is higher than 2, etc.

Precision: Simple arithmetic allows for great precision in using numbers. One meter is exactly 100 cm, and so forth.

Hansen and Porter specifically analyse barcodes and radio frequency identification numbers in this light, but their insights are more broadly relevant, certainly in the tax world. A few real-world examples to illustrate this point:

Citizens of Tax Justice’s “Offshore Shell Games”

For the American tax think tank/advocacy organisation, Citizens of Tax Justice, the stated organisational focus is “federal, state and local tax policies and their impact upon our nation.” A wide span. And each year, they output hundreds of reports, blogs, analysis, etc. on these topics. Yet, since 2013, their most popular annual output has arguably been the “Offshore Shell Games” report, which surveys the deferred cash stock of US multinationals in tax haven subsidiaries. The vast amounts, up to $2.5 trillion per the 2016 report, and its “leaderboard” of offshore cash piles make for neat media stories.

The $2.5 trillion number is mobile, straight-to-the-point, easy to communicate, thus become widely cited, shared and discussed. The massive figure is also stable, eyecatching yet unmistakable, and leverages the combinability of numbers, being made up of similarly clear individual company numbers, plainly extracted from corporate accounts. Order is key: As we see the number go up each year, from $2 to $2.1 to $2.5 trillion, the simple narrative is one of growth, which CTJ firmly problematises. And finally, precision of the number provides strength: with the bottom-up data approach from corporate accounts readily accessible and the overall number down to a decimal point, the credence of the overall argument is enhanced. Together, these features make for a plain and intuitive case, allowing CTJ to successfully set out the argument that “big US multinationals are hoarding monumental cash piles offshore, avoiding billions in US taxes.”

Tax Foundation’s Tax and Growth predictions

Another American tax research organisation, the Tax Foundation, has been hitting the headlines recently. To be fair, it has been a lot since establishment in 1937. But arguably their current popularity has been driven primarily by data outputs. Of particular note is its “Tax and Growth Model” outputs, a macroeconomic dynamic scoring model used to evaluate economic effects of policy changes. The model is similar to macroeconomic models used by the US government (and other governments), though it allows for less conservative dynamic scoring, meaning behavioural and thus economic effects of policy changes are factored in to a much greater extent (a topic of much debate). (In Denmark, the centre-right/liberal think tank CEPOS works in similar ways, though they often rely on the independent DREAM model, and they work more broadly than tax issues). In short, the model allows you to input policy changes and receive a range of estimates about the consequent economic future. This is what allowed the Tax Foundation to ascertain during the American election campaign that Donald Trump’s tax plan would generate 10-year GDP growth of 8.2%, compared to Hillary Clinton’s -2.6%.

(The list of noteworthy data outputs also includes the Foundation’s US “State Business Tax Climate Index”, which compares and ranks each state’s business tax systems – garnering significant public and policy attention. Such rank comparisons are incredibly powerful, similar to PwC and the World Bank’s international “Paying Taxes” ranking – I’ll discuss why in the third example below.)

Again the numbers there are mobile and stable; they are ease to carry across debates and borders and simple for any recipient to comprehend. The order is central here, too: 8.2 is clearly superior to than -2.6. Combinality is a core component of the GDP estimates; they are made up of hundreds of assumptions and calculations on the effects of policy changes, possible only through such a massive scoring model. Finally, precision is paramount. We are provided with one single, definitive prediction of the GDP effects of a catalogue of tax policy changes, down to a decimal point, providing a true aura of conviction. Hundreds of assumptions, based on a vast economic theory and empirical research, boiled down. The final number provides the ultimate appearance of precision and certainty. In all, the number characteristics contribute to laying the ground for a simple conclusion: “Trump’s tax plan is better for the economy than Clinton’s”.

Tax Justice Network’s “Financial Secrecy Index”

The Tax Justice Network, another productive tax advocacy/research organisation, has been highly successful with its key number ranking output: the Financial Secrecy Index. The FSI compares jurisdictions around the world based on financial secrecy and size. The FSI, published first in 2009, then 2011, 2013 and 2015, surveys 15 key secrecy indicators (legal rules, administrative practice, etc.) and the overall national financial services market, which rolls up into a final global ranking of “secrecy jurisdictions”: 1, 2, 3, and so forth. The size feature was included in particular in order to challenge prevalent understandings of “tax havens” as small offshore island states. Instead, the USA, the UK, even Germany and Japan, have ended up high on the secrecy score, prompting headlines back in 2009 like, “Delaware – a black hole in the heart of America“.

Looking once more to the distinct characteristics of numbers, we can see the importance. The US being the number 1 (or number no. 3, in the 2016 report) most secrective jurisdiction in the world is a firm, easy-to-grasp statement, easy to transmit and hard to misunderstand. It provides a strong challenge to conventional wisdsom on tax havens. Combinability has also been central to one important FSI story. The anatomy of the FSI score is of course a combined number. Further, if you combine the scores for the UK, its overseas territories and crown dependencies, it would top the list – which has been one of the key media stories following the FSI launch. Order, of course, is the key ingredient to a ranking, and provides a powerful messaging tool. Number 1 is number 1, number 2 is behind, ahead of number 3, etc. The ability to say, based on an extensive data exercise, that “Switzerland is the top secrecy country in the world”, and “the US is (one of) the top secrecy jurisdictions in the world”, is highly impactful. Finally, the precise scoring on multiple indicators, allows for the appearance of a strong representation of reality: Switzerland’s 2016 FSI score is almost double that of Luxembourg – potentially a damning indictment.

Numbers affect tax policy, but not always

Numbers matter for tax policy. Much like the technicisation of international tax reform, the number-obsession creates participation barriers to policy debates and shapes politics and policies, with important democratic, economic and social implications. And much like the reliance of modern academic economists and indeed public and international economists on econometrics and mathematical modelling has become necessary and crucial in order to be taken seriously and impact economic policy debates, the ability of tax policy actors to draw upon quantitative support for tax policy arguments has become essential.

Indeed, numbers push arguments, emphasise issues and change perceptions. The $2.5 trillion dollar offshore cash pile guides attention towards US deferral reform and MNC tax behavioural norms. The 10%-point swing in GDP growth estimates tells us that one Presidential candidate’s proposals are significantly better for the economy. And the secrecy jurisdiction ranking persuade us that, despite conventional wisdom, the US and the UK are among the key enablers of ‘the offshore world’.

Policy actors able to produce, harness, manage and broker quantitative support for policy arguments thus stand at a potentially significant advantage in tax debates, with the possibility to effect real policy change.

However, it is important to recognise that not all numbers or data-based arguments are successful and influential. The datafication of policy debates is certainly just one part of the equation. Numbers are assessed and filtered through policy processes and debate arenas, and some are certainly less robust and authoritative than others. Maya Forstater, for instance, has done yeoman’s work to dispel the precision of certain ‘Wild Ass Guesses‘ within the tax policy area. When arguments with quantitative support are successful, it is often not entirely because of the quantitative element itself; often, it is because those proposing the data-backed arguments are also able to draw on strong expertise and key networks (as I have argued elsewhere). For instance, the ability of the Tax Justice Network to draw on credible expertise to build and push the Financial Secrecy Index as a global benchmark was a key success factor in changing the discourse around “offshore tax havens”.

Thus, the next time you observe a tax policy debate (or indeed other policy debates), pay attention to the use of numbers, data and quantitative support. And see how it is used, leveraged and combined with other factors, such as credible expertise and networks. Often, this is how policy arguments are won and lost.